Coronary artery stenosis, plaque burden and severity of myocardial ischemia

Aim The relationship between the extent and composition of coronary atherosclerosis and the severity of myocardial ischemia remains incompletely understood. We assessed whether artificial intelligence-guided coronary computed tomography angiography (AI-QCT)–derived plaque burden and composition correlate with ischemia severity. Methods and results We included 837 symptomatic patients undergoing coronary CTA and subsequent 15O-water PET myocardial perfusion imaging. AI-QCT was used to quantify plaque features—diameter stenosis, percent atheroma volume (PAV), percent non-calcified plaque volume (NCPV), and percent calcified plaque volume (CPV)—per patient and per major coronary artery (LAD, LCx, RCA). Ischemia severity was classified into four categories based on regional hyperaemic myocardial blood flow. Increasing severity of ischemia was associated with higher diameter stenosis and plaque burden (PAV, NCPV, CPV) on patient level and in all major coronary territories (overall p < 0.001). The LAD consistently demonstrated higher atherosclerotic burden as compared to the LCx and RCA. Ordinal logistic regression confirmed that diameter stenosis (OR 1.02-1.03, p<0.001) and NCPV (OR 1.04-1.05, p=0.011-0.031) were significant predictors of ischemia severity in all coronary arteries, while CPV was predictive only in the LAD and RCA (OR 1.03-1.04, p=0.002-0.015). Conclusions AI-QCT-derived measures of plaque burden and stenosis are associated with the severity of myocardial ischemia, although overlapping distributions across ischemia severity indicate that anatomical imaging alone may be insufficient for accurate phenotyping of flow-limiting CAD. These findings encourage for the integration of functional imaging with quantitative plaque analysis for a more comprehensive evaluation of coronary artery disease.